On Shapley Value in Data Assemblage Under Independent Utility

TL;DR

This paper introduces a method for efficiently computing the exact Shapley value in data marketplaces under the assumption of independent utility, significantly reducing computation time while maintaining accuracy.

Contribution

It formulates the independent utility assumption in data assemblage, enabling fast and exact Shapley value computation, which was previously computationally challenging.

Findings

Exact Shapley values computed efficiently under independent utility

Significant speedup in computation compared to traditional methods

Validated on benchmark datasets with accurate and faster results

Abstract

In many applications, an organization may want to acquire data from many data owners. Data marketplaces allow data owners to produce data assemblage needed by data buyers through coalition. To encourage coalitions to produce data, it is critical to allocate revenue to data owners in a fair manner according to their contributions. Although in literature Shapley fairness and alternatives have been well explored to facilitate revenue allocation in data assemblage, computing exact Shapley value for many data owners and large assembled data sets through coalition remains challenging due to the combinatoric nature of Shapley value. In this paper, we explore the decomposability of utility in data assemblage by formulating the independent utility assumption. We argue that independent utility enjoys many applications. Moreover, we identify interesting properties of independent utility and develop fast computation techniques for exact Shapley value under independent utility. Our experimental results on a series of benchmark data sets show that our new approach not only guarantees the exactness of Shapley value, but also achieves faster computation by orders of magnitudes.